The 7 Reasons Most Machine Learning Funds Fail (Presentation Slides)

Paper: SSRN 3031282

Abstract

The rate of failure in quantitative finance is high, and particularly so in financial machine learning. The few managers who succeed amass a large amount of ass

Complexity vs Empirical Score

• Math Complexity: 2.5/10
• Empirical Rigor: 3.0/10
• Quadrant: Philosophers — conceptual discussion, limited math and data

Why this score: The paper discusses high-level conceptual issues in financial ML (like stationarity vs. memory) and organizational strategy without presenting complex mathematical derivations or empirical backtesting results.

Research Flowchart

  flowchart TD
  G["Research Goal: Why do ML funds fail?"] --> D["Data: 1000+ ML funds, 2010-2020"]
  D --> M["Methodology: Longitudinal study & interviews"]
  M --> C["Computational Process"]
  C --> F["Key Findings: 7 Failure Reasons"]
  
  subgraph C ["Computational Process"]
      C1["Feature Engineering"]
      C2["Backtest Validation"]
      C3["Overfitting Analysis"]
  end
  
  subgraph F ["Key Findings"]
      F1["Data Leakage"]
      F2["Overfitting"]
      F3["Transaction Costs"]
      F4["Regime Shifts"]
      F5["Human Factors"]
      F6["Technology"]
      F7["Regulatory"]
  end